One overarching problem common to all these techniques is the fact that the convolutional feature extractors used to learn the metric spaces produce feature maps characterizing appearance at a grid of spatial locations, whereas the chosen distance functions require a single vectorial representation for the entire image.
The researcher must decide how to convert the feature map into a vector representation.
Optimally, this conversion would preserve the spatial granularity and detail of the feature map without overfitting to pose, but existing, widely-employed approaches do not accomplish this.
Global average-pooling, the standard solution for parametric softmax classifiers, averages information from disparate parts of the image, completely discarding spatial details that might be necessary for fine distinctions.
Flattening the feature map into a single long vector preserves the individual features [1]}, [2]}, but also encodes the explicit location of each feature.
This sensitivity to feature location and arrangement (i.e., object pose), regardless of underlying semantic content, is highly undesirable.
Larger and more responsive receptive fields will reduce this sensitivity, but instead overfit to specious cues [3]}.
We aim to avoid these tradeoffs entirely, preserving spatial detail while disentangling it from location.
